Improvement of newly diagnosed immune thrombocytopenia management in children by implementing revised practice guidelines
Bibliographic record
Abstract
Abstract Objectives Newly diagnosed immune thrombocytopenia is typically transient, with severe bleeding rarely occurring despite low platelet counts. In the absence of significant bleeding, observation without treatment is considered safe in children. Despite guidelines prioritizing bleeding scores over platelet counts to guide treatment decisions, this practice is not widely adopted. The safety and benefit of modifying local practices to follow these guidelines remain unclear. The objective was to describe treatment and hospitalization rates after guideline implementation and to assess the occurrence of complications and persistent immune thrombocytopenia in the POST group compared with the PRE group. Methods The new algorithm recommended treatment for patients with Buchanan score ≥3 regardless of platelet count, while the previous one suggested treatment if platelet count <10 × 109/L. We compared patient management before (PRE: January 2019–December 2020) and after (POST: January 2022–December 2023) implementation of new local guidelines in the paediatric emergency department of Hospital Sainte-Justine, Quebec. Results We included 84 patients: 34 PRE and 50 POST with comparable characteristics. In the POST period, the guideline was followed in 88% of the cases, more patients were observed without treatment (PRE 24%, POST 58% P = 0.002) and less were hospitalized at diagnosis (PRE 62%, POST 22% P = 0.0005). The effect was mainly due to a higher observation rate in patients with low bleeding score (PRE 32%, POST 84%). No intracranial bleeding, death, or increased persistent ITP rate was observed. Conclusion In sum, transitioning from platelet-based to bleeding score based guidelines was safe, reduced treatment use in the emergency department, and decreased hospitalizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".